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Register a new userCosmic Microwave background map making with data between 10 and 90GHz
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Authors
A.W. Jones
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We use data from the Tenerife 10, 15 and 33 GHz beamswitching experiments along with the COBE 53 and 90 GHz data to separate the cosmic microwave background (CMB) signal from the Galactic signal and create two maps at high Galactic latitude. The new multi-MEM technique is used to obtain the best reconstruction of the two channels. The two maps are presented and known features are identified within each. We find that the Galactic contribution to both the 15 and 33 GHz Tenerife data is small enough to be ignored when compared to the errors in the data and the magnitude of the CMB signal.
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Simulated observations of a $10dg times 10dg$ field by the Microwave Anisotropy Probe (MAP) are analysed in order to separate cosmic microwave background (CMB) emission from foreground contaminants and instrumental noise and thereby determine how accurately the CMB emission can be recovered. The simulations include emission from the CMB, the kinetic and thermal Sunyaev-Zeldovich (SZ) effects from galaxy clusters, as well as Galactic dust, free-free and synchrotron. We find that, even in the presence of these contaminating foregrounds, the CMB map is reconstructed with an rms accuracy of about 20 $mu$K per 12.6 arcmin pixel, which represents a substantial improvement as compared to the individual temperature sensitivities of the raw data channels. We also find, for the single $10dg times 10dg$ field, that the CMB power spectrum is accurately recovered for $ell la 600$.
Powerful constraints on theories can already be inferred from existing CMB anisotropy data. But performing an exact analysis of available data is a complicated task and may become prohibitively so for upcoming experiments with gtrsim10^4 pixels. We present a method for approximating the likelihood that takes power spectrum constraints, e.g., ``band-powers, as inputs. We identify a bias which results if one approximates the probability distribution of the band-power errors as Gaussian---as is the usual practice. This bias can be eliminated by using specific approximations to the non-Gaussian form for the distribution specified by three parameters (the maximum likelihood or mode, curvature or variance, and a third quantity). We advocate the calculation of this third quantity by experimenters, to be presented along with the maximum-likelihood band-power and variance. We use this non-Gaussian form to estimate the power spectrum of the CMB in eleven bands from multipole moment ell = 2 (the quadrupole) to ell=3000 from all published band-power data. We investigate the robustness of our power spectrum estimate to changes in these approximations as well as to selective editing of the data.
MADmap is a software application used to produce maximum-likelihood images of the sky from time-ordered data which include correlated noise, such as those gathered by Cosmic Microwave Background (CMB) experiments. It works efficiently on platforms ranging from small workstations to the most massively parallel supercomputers. Map-making is a critical step in the analysis of all CMB data sets, and the maximum-likelihood approach is the most accurate and widely applicable algorithm; however, it is a computationally challenging task. This challenge will only increase with the next generation of ground-based, balloon-borne and satellite CMB polarization experiments. The faintness of the B-mode signal that these experiments seek to measure requires them to gather enormous data sets. MADmap is already being run on up to $O(10^{11})$ time samples, $O(10^8)$ pixels and $O(10^4)$ cores, with ongoing work to scale to the next generation of data sets and supercomputers. We describe MADmaps algorithm based around a preconditioned conjugate gradient solver, fast Fourier transforms and sparse matrix operations. We highlight MADmaps ability to address problems typically encountered in the analysis of realistic CMB data sets and describe its application to simulations of the Planck and EBEX experiments. The massively parallel and distributed implementation is detailed and scaling complexities are given for the resources required. MADmap is capable of analysing the largest data sets now being collected on computing resources currently available, and we argue that, given Moores Law, MADmap will be capable of reducing the most massive projected data sets.
We present the first sky maps from the BEAST (Background Emission Anisotropy Scanning Telescope) experiment. BEAST consists of a 2.2 meter off axis Gregorian telescope fed by a cryogenic millimeter wavelength focal plane currently consisting of 6 Q band (40 GHz) and 2 Ka band (30 GHz) scalar feed horns feeding cryogenic HEMT amplifiers. Data were collected from two balloon-borne flights in 2000, followed by a lengthy ground observing campaign from the 3.8 Km altitude University of California White Mountain Research Station. This paper reports the initial results from the ground based observations. The instrument produced an annular map covering the sky from declinateion 33 to 42 degrees. The maps cover an area of 2470 square degrees with an effective resolution of 23 arcminutes FWHM at 40 GHz and 30 arcminutes at 30 GHz. The map RMS (smoothed to 30 arcminutes and excluding galactic foregrounds) is 54 +-5 microK at 40 GHz. Comparison with the instrument noise gives a cosmic signal RMS contribution of 28 +-3 microK. An estimate of the actual CMB sky signal requires taking into account the l-space filter function of our experiment and analysis techniques, carried out in a companion paper (ODwyer et al. 2003). In addition to the robust detection of CMB anisotropies, we find a strong correlation between small portions of our maps and features in recent H$alpha$ maps (Finkbeiner, 2003). In this work we describe the data set and analysis techniques leading to the maps, including data selection, filtering, pointing reconstruction, mapmaking algorithms and systematic effects. A detailed description of the experiment appears in Childers et al. (2003).
We cross-correlate the cosmic microwave background temperature anisotropy maps from the WMAP, MAXIMA-I, and MAXIMA-II experiments. We use the cross-spectrum, which is the spherical harmonic transform of the angular two-point correlation function, to quantify the correlation as a function of angular scale. We find that the three possible pairs of cross-spectra are in close agreement with each other and with the power spectra of the individual maps. The probability that there is no correlation between the maps is smaller than 1 * 10^(-8). We also calculate power spectra for maps made of differences between pairs of maps, and show that they are consistent with no signal. The results conclusively show that the three experiments not only display the same statistical properties of the CMB anisotropy, but also detect the same features wherever the observed sky areas overlap. We conclude that the contribution of systematic errors to these maps is negligible and that MAXIMA and WMAP have accurately mapped the cosmic microwave background anisotropy.
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